使用紋理能量圖和卷積神經網路在二維條碼偵測

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姓名

柯羽航(Halin Ke) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

使用紋理能量圖和卷積神經網路在二維條碼偵測

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至系統瀏覽論文 (2024-6-17以後開放)

摘要(中)

Data-Matrix二維條碼因其抗損毀能力強、所佔空間小等特性，已被廣泛應用於航太工業、汽車製造業、半導體與印刷電路板零件的識別。條碼偵測是條碼辨識的關鍵。針對低品質的條碼影像，例如扭曲、模糊、光照不均等，典型的條碼偵測方法會有偵測率過低和辨識困難的問題。本研究提出一個創新的條碼偵測方法，基於Data-Matrix二維條碼其邊緣取向具有相互垂直的特性，將條碼影像轉換成紋理能量圖，再結合卷積神經網路進行條碼偵測模型的深度學習。我們使用一個低品質Data-Matrix條碼影像資料庫來驗證所提出的方法，其條碼偵測正確率可以提升22%，可有效改善條碼辨識性能不足的問題。

摘要(英)

Data-Matrix 2D barcodes have been widely used in the identification of aerospace industry, automotive industry, semiconductor and printed circuit board parts due to their strong resistance to damage and small space. Barcode detection is the key to barcode recognition. For low-quality barcode images, such as distortion, blur, uneven illumination, etc. The typical barcode detection methods have problems of low detection rate and difficulty in identification. This study proposes an innovative barcode detection method based on Data-Matrix two-dimensional barcode whose edge orientation has mutually perpendicular characteristics, converts the barcode image into a Texture Energy Map, and combines the depth learning of the barcode detection model with the Convolutional Neural Network. We use a low-quality Data-Matrix barcode image database to verify the proposed method. The barcode detection accuracy can be improved by 22%, which can effectively improve the problem of insufficient barcode recognition performance.

關鍵字(中)

★ Data-Matrix二維條碼
★ 紋理能量圖
★ 卷積神經網路

關鍵字(英)

★ Data-Matrix barcode
★ Texture Energy Map
★ Convolutional Neural Network

論文目次

致謝 1
摘要 1
Abstract 1
目錄 1
圖目錄 1
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 3
第二章、相關技術 4
2.1 二維條碼偵測 4
2.1.1 邊緣偵測 4
2.1.2 紋理分析 7
2.1.3 型態學運算 10
2.2 卷積神經網路物件偵測 11
2.2.1 R-CNN 12
2.2.2 Fast R-CNN 13
2.2.3 Faster R-CNN 14
2.2.4 YOLO 16
第三章、系統設計 20
3.1 Data-Matrix二維條碼偵測系統架構 21
3.2 影像前處理模組 21
3.2.1 梯度強化 22
3.2.2 邊緣偵測 23
3.2.3 紋理能量圖 23
3.2.4 影像前處理離散事件建模 24
3.3 YOLO條碼偵測模組 25
3.4 條碼影像標註 31
3.4.1 標註步驟 32
3.4.2 標註規則 36
3.4.3 YOLO訓練集整理 37
第四章、實驗 39
4.1 實驗平台與方法 39
4.2 YOLO模型訓練實驗 42
4.3 YOLO模型訓練集數量實驗 45
4.4 LibDmtx條碼偵測實驗 47
4.5 實驗比較與分析 48
第五章、結論與未來展望 54
5.1 結論 54
5.2 未來展望 55
參考文獻 56

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指導教授

陳慶瀚(Pierre Chen)

審核日期

2019-7-4

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